The syllabus promised “Team practicum: one or more groups identify a research project and make several weeks of progress”. Our goal is to implement that in a way that gives everyone a meaningful opportunity to engage with the research area and demonstrate some accomplishment, involving an amount of time appropriate for a 1.5 credit course.
11/5. Brainstorming homework. Everyone submits 2 or more potential project ideas. Some suggestions to help with this are given below.
11/6. Class discussion of project ideas and team matching. We discuss the proposed projects and select teams, aiming to pick tasks that can be completed in 2-3 weeks with everyone having an assigned role and committing at least a full day of work.
11/13. Progress report. Everyone will report on what they have been doing. Each group should submit 1 slide to present. If you are taking the computation option, it would be good to show some concrete progress, such as successfully running some code provided in a published paper.
11/20. First half of the final presentations. Assuming groups of size 2, we will have 3 presentations per class, each lasting 25 min. The presentation should involve slides and a guided discussion (which you will guide) following the style of lectures, except that you won’t be able to assume that everyone has previously read a paper on your specific topic.
12/4. Second half of the final presentations.
Further reading. A list of potentially suitable papers is listed below. You can pick from this list, and study the paper.
Computation. It is difficult to know how far one can reasonably get into carrying out a phylodynamic analysis (on simulations or data) in the timeframe available. A natural starting point is to take a published paper that is supplied with code, start by running that code (which can involve nontrivial set-up tasks) and then start perturbing it to explore alternative choices.
For those who are in the course because they have experiences in closely related research topics, a natural project is to investigate that relationship (e.g,, by finding and identifying a relevant paper) and present it to the class. This is not the only option. If you would rather dive deeper into the main focus of this course, that is also welcome. If you propose a project of this kind in your brainstorming homework, you should ideally be willing to help a team-mate interested in collaborating on your topic.
We can add to this list. In particular, you may come up with additional suggestions by making some Google Scholar investigations during your brainstorming.
An alternative option is to dig deeper into papers we have already read, perhaps identifying a particular part of the theory or results that you think is worth investigating in more detail.
Park, Y., & Koelle, K. (2025). Common misspecification of the generation interval leads to reproduction number underestimation in phylodynamic inference. bioRxiv, 2025-04. https://doi.org/10.1101/2025.04.28.649807
Andréoletti, J, Zwaans, A, Warnock, RCM, Aguirre-Fernández, G, Barido-Sottani, J, Gupta, A, Stadler, T, & Manceau, M. The occurrence birth–death process for combined-evidence analysis in macroevolution and epidemiology. Systematic Biology syac037 (2022). https://doi.org/10.1093/sysbio/syac037
Attwood, SW, Hill, SC, Aanensen, DM, Connor, TR, & Pybus, OG. (2022). Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nature Reviews Genetics 23:547–562. https://doi.org/10.1038/s41576-022-00483-8
Damodaran, L, Jaeger, A, & Moncla, LH. (2024). Intensive transmission in wild, migratory birds drove rapid geographic dissemination and repeated spillovers of H5N1 into agriculture in North America. bioRxiv 2024.12.16.628739. https://doi.org/10.1101/2024.12.16.628739
De Maio, N, Wu, CH, O’Reilly, KM, & Wilson, D. (2015). New routes to phylogeography: a Bayesian structured coalescent approximation. PLOS Genetics 11:e1005421. https://doi.org/10.1371/journal.pgen.1005421
Vaughan, TG, Kühnert, D, Popinga, A, Welch, D, & Drummond, AJ. (2014). Efficient Bayesian inference under the structured coalescent. Bioinformatics 30:2272–2279. https://doi.org/10.1093/bioinformatics/btu201
Thompson, A, Liebeskind, BJ, Scully, EJ, & Landis, MJ. (2024). Deep learning and likelihood approaches for viral phylogeography converge on the same answers whether the inference model is right or wrong. Systematic Biology 73:183–206. https://doi.org/10.1093/sysbio/syad074
Voznica, J, Zhukova, A, Boskova, V, Saulnier, E, Lemoine, F, Moslonka-Lefebvre, M, & Gascuel, O. (2022). Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks. Nature Communications 13:3896. https://doi.org/10.1038/s41467-022-31511-0